Application of PSO-RBF Neural Network in Network Intrusion Detection

Abstract

Detecting all kinds of intrusions efficiently is significant to network security. Radial basis function (RBF) neural network is a kind of feed forward neural network, which is widely employed as a real-time pattern classification. In RBF neural network, the center of radial basis function, the variance of radial basis of function and the weight have to be chosen. If they are not appropriately chosen, the RBF neural network may degrade validity and accuracy of modeling. Particle swarm optimization algorithm (PSO) is a member of the wide category of swarm intelligence methods to solve non-linear programming problems. PSO has proved to be competitive with genetic algorithm (GA) in parameter optimization. So PSO is used to optimize the RBF neural network parameters in this work. Therefore, the novel combination method based on RBF neural network and PSO (PSO-RBFNN) is adapted to network intrusion detection. The experimental results show that the proposed model is superior to the conventional RBF neural network.